Visualization

Which album is this song from? Algorithm is baffled by Rammstein


k-Nearest Neighbour (k-NN) classification algorithm has been applied to the dataset in order to classify the songs into albums using computational methods. If the predictions are accurate, we can infer that albums significantly differ from each other, which would further aid my Mutter/Sehnsucht vs Rosenrot debate.

Unfortunately, k-NN performs poorly on Rammstein’s discography. The best obtained score is 5/11 songs correctly classified, that is for Herzeleid. Precision and recall scores, summarized below, indicate an abundance of both false negatives and false positives. For instance, for RAMMSTEIN album, both precision and recall are 0.

Low recall means that songs from a particular album are not categorized into the same album by the algorithm. Low precision means that the algorithm also thinks that a lot of songs from other albums belong to an album in question. The only acceptable scores are for Herzeleid, although recall is still quite poor.

Album (Precision, Recall):
- Herzeleid (0.7142857, 0.4545455)
- LIEBE IST FÜR ALLE DA (0.2500000, 0.1875000)
- Mutter (0.1428571, 0.1818182)
- RAMMSTEIN (0.0000000, 0.0000000)
- Reise, Reise (0.2000000, 0.1818182)
- ROSENROT (0.2307692, 0.2727273)
- Sehnsucht (0.2500000; 0.2727273)

Choosing top predictors (loudness, c12, c04) and features used for previous analyses (danceability, energy, valence, loudness and tempo) did not majorly improve things.

Training a classifier using so many categories (Audio Features, timbre components), whilst having a small corpus (82 songs) will most probably lead to poor performance, as the variability is extremely high. No definite conclusions can be drawn from k-NN results.

Hierarchical k-Means clustering: Sehnsucht & Mutter are Neighbours, Rosenrot is their Distant Friend


K-NN is a supervised learning algorithm that is useful for classification and regression problems. As was seen from the previous tab, the performance is lacking.

I have decided to use an unsupervised learning algorithm made to tackle clustering problems, that is hierarchical k-means.

To begin with, I wanted to know whether it is sensible to conduct these analyses at all. I generated a distance matrix to see if there are any preliminary clustering trends. I decided to focus on the features that were determined to be potentially characteristic after analyzing the density plots in previous tabs. The distance matrix on the left based on danceability, energy, valence, loudness and tempo shows the Euclidean distance between album pairs. The closer to 0 (black colour), the less distance, thus the more similar are albums. There is a black diagonal line that indicates that albums are identical, that is distance = 0, because we are comparing an album to itself.

The matrix shows that Rosenrot seems to be similar to RAMMSTEIN, whereas Sehnsucht is quite different from every other album. Looks promising!

The results of the hierarchical k-means can be seen in the second plot. The algorithm grouped similar objects together. Objects within each cluster are more similar to each other than to those outside the cluster.

The amount of clusters (3) was chosen after applying the elbow method. In this method, sum of squares at each number of clusters (in my case, from 2 to 5) are calculated and plotted. Afterwards, by determining the “elbow” (a point at which the slope changes from steep to shallow), the user can determine an optimal number of clusters. This method is not as precise as other, more mathematically-rigorous cluster validation methods, but seemed to work for the current (small) dataset.

Indeed, as can be seen from both the distance matrix and hierarchical k-means clustering, Sehnsucht and Mutter share more similarities with each other than with Rosenrot, which is grouped with RAMMSTEIN, which supports the idea that Rosenrot is somehow different from Mutter and Sehnsucht.

Valence and Energy in context: Rammstein is angry, but also happy and not calm at all


Spotify’s Audio Features sometimes might seem a bit isolated from the real world. Valence is said to be “a measure describing the musical positiveness conveyed by a track”. The higher the valence score, the more positive, happy and cheerful the song sounds. Energy is labelled as “perceptual measure of intensity and activity”. Tracks with high energy are typically fast, loud and noisy.

But how perceptually sound these measures really are?

To tackle this question, I decided to look at the corpus in the context of valence-arousal model of emotion (circumplex model). In this model, emotions are distributed in a 2D circular space. The dimensions are arousal (Spotify’s energy) and valence. The combinations of these features result in 4 emotion quadrants - calm, happy, sad or angry. Placing Rammstein’s songs in this model can provide us with an overview of the emotions that their music evokes.

The results of the classification are quite fascinating. Firstly, Rammstein has no calm songs, as the quadrant is absolutely empty. This is not unexpected, as Rammstein has only few ballads and those are in the calm category. The songs that are the closest to being calm are Los and Laichzeit. Perceptually, these two tracks might sound more monotone compared to others in happy, which places them lower on the energy scale, but they are still loud and fast. It is also a bizarre and grim experience seeing a song about incest and bestiality (Laichzeit) in the happy category…

Secondly, the happiest songs are Bestrafe mich and Eifersucht. Please take a listen and judge yourself whether these songs sound happy! I would guess that they do not. To me, Ausländer is the happiest track out of the whole discography, but it is in the angry quadrant. These misalignments might indicate that Spotify’s features do not directly correspond to arousal and valence of the circumplex model.

The angriest song is Rammlied, which I agree with. This powerful album opening song has an apocalyptic vibe to it, with a pronounced bass guitar and devilish Till’s vocals juxtaposed with church choir in the background. This is a grandstanding song that just sounds powerful and furious.

Unsurprisingly, Ein Lied is the most unhappy song. Ein Lied is one of their slowest songs, with acoustic guitars, ethereal synthesizers and theremin-like keyboards being the focus of the song’s instrumental. The percussion is completely absent from this song. The vocal line is soft, partly spoken, partly sung using low breathy tones, which is atypical of Rammstein.

Overall, the majority of songs are in the angry category and none in calm, which, in my opinion, aligns with the public’s perception of Rammstein and metal music in general. No specific trends per album can be seen.

Rammstein’s preferred tempo: does it align with Moelants’?


Moelants (2002) showed that there is a certain preferred tempo at around 125 BPM. This natural tempo corresponds with both repeated motor actions, such as finger tapping, and perceived tempo in musical data. As can be seen from the plot, Rammstein’s songs mostly fall in 115-140 BPM frame, with a visible peak at 120-130 BPM.

Moelants’ findings, current corpus’ tempo distribution and Spotify’s own value distribution for tempo align pretty neatly, with noticeable spikes at 120-130 BPM marks.

Although metal music can cover a wide scale of tempos, preferred tempo range is still reflected in Rammstein’s discography, further indicating that it indeed might be more natural to compose songs in that tempo range.

Tempograms: Ich will is faster than Feuer frei!?


The two cyclic tempograms at the left illustrate Spotify’s estimated tempo for two typical songs: Feuer Frei! and Ich will. Overall, the estimated tempo (yellow line) is strong and stable throughout the duration of both songs. The excerpts where the line breaks correspond to fragraments of the songs where minimal instrumental accompaniment is present.

Interestingly, Feuer Frei! sounds way faster to me than Ich will. I would even say that Feuer Frei! is one of the fastest songs in the corpus, whereas Spotify claims that it is actually on the slower side. My personal estimation of tempo for Feuer Frei! would stand at around twice Spotify’s - 190 BPM. Ich will’s BPM makes sense, as it is indeed ~128 BPM.

Because Spotify’s BPM estimation might be based on tatums, which are subdivisions of beats, we get this inaccurate tempo. In short, if there is one particular tempo, Spotify probably captures twice and thrice the BPM. Spotify most probably chose the lower band (tempo subharmonic), which we now see on the Feuer frei! tempogram - 95 BPM, whereas the true tempo is twice as fast - 190 BPM.

Here we saw an example of Spotify API’s faulty judgement, along with an accurate one. Spotify’s beat tracking technique is not bulletproof!

Popularity: Sehnsucht and Mutter are still relevant, but is Rosenrot?


To take as much as I can from Spotify’s available data, I also explored the popularity metric. Spotify is a bit secretive about the algorithm behind popularity scores, so it is helpful to illustrate datapoints.

Spotify provides popularity scores on 3 levels: track, album and artist. All 3 levels can be seen on the interactive plot on the left. Feel free to explore and see where your favorite songs land!

Spotify’s documentation page tells us that “the value is between 0 and 100, with 100 being the most popular. The track popularity is calculated by algorithm and is based, in the most part, on the total number of plays the track has had and how recent those plays are. Artist and album popularity is derived mathematically from track popularity.”

In theory, if Rosenrot is the least favoured album, the popularity score on all levels should be the lowest among the discography. Conversely, Mutter and Sehnsucht should have the highest ratings. So does Spotify’s data actually support this hypothesis?

The answer is yes and also no. On the track level, Rosenrot does not contain only unpopular songs. On the album level, however, Rosenrot is the second least popular album (68), with Herzeleid occupying the first place. One can argue that the recency factor came into play here. Herzeleid (1995) was the band’s debut album, which came out 25 years ago. Rosenrot (2005) is relatively new. I think it is a bit tricky to compare such old albums to newer ones; still, Herzeleid’s median is higher (54) than Rosenrot’s (50). Moreover, album-level popularity differs by only 1 point.

As for Mutter (2001) and Sehnsucht (1997), although both of the albums were released a while ago, they are still very popular on both track and album levels. On the album level, Mutter (75) and Sehnsucht (74) share the second and the third place, respectively, overtaken only by RAMMSTEIN. RAMMSTEIN’s popularity might be explained by the fact that it is their most recent album (2019).

If we take a look at the track level, Mutter’s median is 60, the same value as RAMMSTEIN’s. Although Sehnsucht’s median is lower, it contains some of the most popular songs in the corpus - Du Hast (72) and Engel (68). Sonne (69), the highest rated song from Mutter is also in the top 5 most popular Rammstein’s songs at the moment.

What is going on with Liebe ist für alle da (2009), though? The tracks are low on the popularity scale, but the album’s popularity is comparatively high (70). These ratings seem counterintuitive. The explanation might be hidden in some Spotify’s algorithm’s pecularity, as the raw popularity is probably scaled separetely per level (track, album, artist).

The most attentive readers might have noticed, that atypical songs - Ein Lied (45, Rosenrot, 2005) and Roter Sand - Orchester Version (31, LIFAD, 2009) are not only the least popular songs from the albums, but also in the whole discography.

All in all, we can see a trend: despite being older albums, Sehnsucht (1997) and Mutter (2001) are more popular than their newer counterpart Rosenrot (2005).

Rammstein sound - are there any constants in the corpus?


After feature overview in the previous tab, more or less homogeneous features throughout the discography were plotted. The plot on the left shows mean feature values per album.

As can be seen, the only truly constant feature is speechiness. Speechiness detects the presence of spoken words, so it is natural that Rammstein’s albums constantly score low, as they contain non-speech-like tracks.

Acousticness was below 0.04 for the first 4 albums, but rose starting Rosenrot. That is not surprising - Rosenrot has their first acoustic ballad, Ein Lied. LIFAD includes Roter Sand, Roter Sand (Orchester Version) and Liese, Rammstein has Diamant.

Instrumentalness is all over the place, as albums do vary in vocal content per song. Liveness detects the presence of an audience in the tracks. The differences can be attributed to background cheers and other noisy effects used, such as marching sounds in Links 2 3 4, and Till’s crowd-like counting in Sonne.

In conclusion, only speechiness can be considered a constant feature throughout Rammstein’s discography, but it is not likely to be a feature that can characterize the Rammstein sound.

Introduction

(A)Typical Rammstein: perception and computation



Song Mode Key Tempo Loudness
Du Hast Minor A 125.11 -6.283
Ich will Major D 128.12 -4.254
Feuer Frei! Major Eb 95.14 -3.974
Hallomann Major Bb 172.00 -5.936
Te Quiero Puta! Major F 159.93 -5.305
Ausländer Major C 125.03 -4.159
Ein Lied Major F 122.32 -19.239
Roter Sand - Orchester Version Major G 82.42 -10.255

For detailed, low-level audio analysis, typical and atypical songs have to be established.

What songs would make you exclaim “yes, of course this is Rammstein!”? Personally, I chose Du hast and Ich will. Of course, this approach is highly subjective and influenced by a plethora of external reasons, such as track popularity, familiarity with the artist, individual biases and tastes. To expand the notion of “typical” to include not only my personal judgements, but also those of a potentially impartial algorithm, standardizes z-scores based on all audio features were computed. The songs that scored nearest to 0, mainly Feuer Frei! and Hallomann were chosen as representatives.

The same approach was taken when selecting atypical songs. In my opinion, Te Quiero Puta! may be the most un-Rammstein song they have ever released. In the context of all their other recordings, this metal-meets-mariachi song sounds novel. Another unique track is Ausländer. It sounds so catchy and pop-y, that it might have been a successful (rock)club song. Unsurprisingly, z-scores (z > 2) indicated that Ein Lied and Roter Sand – Orchester Version differ from the rest of the corpus, as these are Rammstein’s ballads.

Summary:
- typical songs - Feuer Frei!, Hallomann, Ich will, Du Hast;
- atypical songs – Ein Lied, Roter Sand – Orchester Version, Te Quiero Puta!, Ausländer.

The Spotify Audio Features per song are presented in an interactive heat table on the left. Additionally, I compiled a playlist of typical/atypical songs! ↓ ADD PLAYLIST!!!!!!!!!!!!!!!!!!!!

Chordograms: Du hast and Te Quiero Puta! are matching


Chordograms show what chords are active at a particular point of time.

Though Du hast is labeled as typical by me, and Te Quiero Puta! atypical, they have a very similar chord profile. In both songs, G#:min, D:maj and F:7 are the most active. Also, around 150 s, both of the chordograms exhibit a yellow line, indicating that the bridge has started.

Te Quiero Puta! is unusual for Rammstein mainly because it is their only song fully written in Spanish. In addition, the brass section (trumpets) is striking and has never been as pronounced as in Te Quiero Puta!; the song has a strong Hispanic flair to it.

Overall, the chord similarity is understandable, as both songs have the Rammstein sound to them, that is heavy, distorted guitars.

Self-similarity matrices (SSM): repetitiveness and clear structure = atypicalness?


On the left, you can see self-similarity matrices, where both x and y axes show seconds. SSMs illustrate pitch- and timbre-based similarity within a chosen track. The line from the bottom left corner to top right corner is a constant, as we are comparing the song to itself. Are there systematic differences, seen SSMs, that differentiate typical and atypical tracks of the corpus?

In chroma SSM for Ausländer (atypical), we can see clear repetitiveness and structure, as there are visible checkerboard patterns, indicating song’s homogeneity. The structure of a song, that is ACBACB, is represented on the plot. Bright yellow lines, seen often in both chroma and timbre SSMs, mark moments of novelty in Ausländer.

Plot for Du hast (typical) tells us a completely different story. Overall, the SSMs are dark, indicating that there are few novel moments throughout the song, mainly 1 - at around 145 s, when there is an almost a silent moment in the song. Hardly anything can be said about the song structure, as there are no patterns seen. Overall timbre is constant; the song is more or less similar timbre-wise.

SSMs have shown that a typical and an atypical song differ in repetition and novelty patterns.

🔥 🤘 Why Rammstein? Get to know RQs and corpus 🤘 🔥

Rammstein is a German Neue Deutsche Härte band known for heavy riffs, thought-provoking, although controversial, lyrics and flame-fuelled live performances.

Almost unanimously, critics and fans alike agree on the worst Rammstein album, which is said to be Rosenrot (2005). Listeners note that the album is “[…] a disjointed effort glued together with some iron-clad bangers” (Chillingworth, 2019) and “[…] feels like a thrown-together collection of B-sides (because, essentially, it was)” (sean_themighty, 2019).

Interestingly, album rankings oftentimes also agree on the best album – Mutter (2001), describing it as “[…] just….legendary” (JonWood007 , 2020). Furthermore, the album is ranked 324 in Rock Hard magazine’s book of The 500 Greatest Rock & Metal Albums of All Time (Rock Hard, 2005). Another strong contender for the title of the most loved Rammstein album, sometimes tied with Mutter, is Sehnsucht (1997). The album contains well-known masterpieces, such as Du hast and Engel.

Additionally, Rammstein has quite a recognizable sound that is usually attributed to distinctive guitars along with Till Lindemann’s baritone and exaggerated trills (so-called rolled r’s).

Thus, in this portfolio, I will investigate whether Spotify’s Audio Features and Popularity Scores can provide insights on the following questions:
1. Why Mutter/Sehnsucht are so well-received, whereas Rosenrot is subject to criticism?
2. Rammstein signature sound: what audio features are constant throughout the discography?
3. Typical and atypical Rammstein: what audio analyses can say about Rammstein’s songs?

Corpus
The corpus consists of released studio albums by Rammstein (7 albums, 82 songs), all of which are available on Spotify:
- Herzeleid (1995)
- Sehnsucht (1997)
- Mutter (2001)
- Reise, Reise (2004)
- Rosenrot (2005)
- Liebe ist für alle da (2009)
- Untitled (or RAMMSTEIN) (2019)

Click through the tabs to find out more!


Audio Feature overview per album 1: keys vary, mode is predominantly major


To potentially pinpoint the differences that might have contributed to Mutter/Sehnsucht love and Rosenrot hate, it is useful to take a close-up look at Spotify’s Audio Features. Furthermore, Audio Features were plotted to see which elements change or stay constant throughout the discography.

The first histogram highlights the key distribution per album. Every Rammstein album has notable variation in keys, averaging at 7 keys per album. Mutter seems to be an outlier, with only 4 keys implemented, mainly D, E♭, E and A. Overall, no clear key preference can be seen throughout the discography.

Second plot is a stacked bar chart that illustrates minor/major modes in the corpus. As can be seen, major mode is preferred throughout all albums. This might be a shocker for some, as Rammstein’s music does not sound necessarily positive, which only further demonstrates that major ≠ happy and minor ≠ sad.

Audio Feature Overview per album 2: distinctive danceability, energy, valence, loudness and tempo


On the left, density plots of Spotify Audio Features for Rammstein’s studio albums are presented. Density plots show smoothed distribution of values and the peaks correspond to locations where there is the highest concentration of said values.

Danceability seems to vary per album. It is clear that Sehnsucht (and Herzeleid) have the highest overall danceability. Conversely, the majority of songs from Rosenrot are located lower on the danceability scale.
Interestingly, danceability is one of the most mysterious Spotify’s Audio Features, as the listener’s perception of danceability for a song sometimes conflicts with the value awarded by Spotify. For instance, Du Riechst So Gut has a danceability score of 0.67/1, whereas I’m Looking Forward to Joining You, Finally (Nine Inch Nails) was awarded with a striking score of 0.795/1.

Energy distribution per album shows that overall, Rammstein’s discography boasts high energy. Sehnsucht’s energy is concentrated at around 0.95, while Rosenrot’s energy peak is seen at 0.7.

Next, valence plot marks Rosenrot as a clear outlier. Low valence corresponds to more sad, angry music.

Another interesting visualization is loudness. Both Rosenrot and Sehnsucht exhibit a strong peak at -5. Although Mutter’s highest density shares the same value, the peak is weak.

Finally, regarding tempo, Rosenrot has more songs in 160 BPM tempo range than other albums.

Speechiness, liveness, acousticness and instrumentalness do not seem to provide any interesting patterns regarding the three albums in question and also overall.

Audio Feature Overview Conclusion: Mutter, Sehnsucht, Rosenrot are going to be analyzed in-depth based on danceability, energy, valence, loudness and tempo, as these features differ per album.

No clue how Rammstein sounds? Take a listen!

For those who are unfamiliar with the band, I have compiled a short (10 song) playlist with my favourite songs.

Listen to “RAMMSTEIN: Beyond Du Hast” playlist here


ADD AT THE END

Chromagrams: energy in E for typical and atypical


The chromagram shows how much energy there is in a certain pitch class per moment of time. Let’s see how different or similar typical and atypical songs’ chromagrams are.

Firstly, all 3 songs’ predominant energy is in class E. It is marked by yellow horizontal bands that can be seen throughout the songs. In Du hast, there is a slight shift towards B in the second part of the song, but E is still active.

Secondly, Roter Sand’s song structure can be easily worked out just by looking at the chromagram. There are well-defined blocks, marking the verses and the chorus, giving the song ABABB structure. For Du hast and Ein Lied, the structure is less clear, as the chromagrams show minimal variation compared to Roter Sand. Thirdly, in general, Du hast’s chromagram has less magnitude.

Conclusion: Spotify is fun and Rammstein is chaotic

In this portfolio, I have explored Rammstein’s discography and tried to unravel the mystery behind album acclaim. In total, 82 songs were used for the analyses, from which 4 typical and 4 atypical songs were chosen.

Here is a summary of the main findings:
- Mutter and Sehnsucht are in the same cluster, whereas Rosenrot is in a separate one, based on danceability, energy, valence, loudness and tempo
- Rosenrot, albeit newer, is less popular than Mutter and Sehnsucht
- only speechiness is truly constant throughout the discography
- there are similarities (chromagrams, chordograms) and differences (SSM) between typical and atypical songs

Based on these findings, I can conclude that lower danceability, lower energy, lower valence, loudness and tempo variations might have contributed to Rosenrot hate. I am emphasizing might, because I do believe that other factors than musical features have a more substantial weight in such popularity distribution. Rosenrot (2004) is basically a b-side album, released 1 year after a succesful Reise, Reise (2004). It was originally intended to be titled Reise, Reise, Vol. II. The album lacks distinctiveness and personality, as it really sounds like a compilation of non-single songs. Sehnsucht and Mutter, on the other hand, contain strong singles (i.e. Engel, Du hast, Sonne, etc.) along with charting b-sides (i.e. Bück dich). This trend also seen in popularity scores. It is important to mention that album success is majorly influenced by marketing, producers, etc.

Also, Rammstein sound could not be attributed to a single Spotify Audio Feature. Rammstein represent the dance-metal genre, which is a cross between alternative metal and electro-industrial music genres. This genre hodgepodge is characterized by predominantly German lyrics, overpronounciation of certain letters (/r/, in this case) and heavily distorted, low guitars. These cues help listeners to identify Rammstein as Rammstein, the pioneers of Tanzmetall. An important extra-musical constant, especially considering that the band has been active for 25+ years, are the group members. The group’s composition hasn’t changed once, which contributes to the continuous Rammstein sound. Comparing Rammstein to other Neue Deutsche Härte/metal bands could provide more information on why their sound is so recognizable. Comparing bands within a genre would also expand this research’s external validity.

Analysis of typical and atypical songs has shown that although the songs differ, they are obviously a part of one artist’s coherent discography. The tempo estimation and popularity exploration indicate that it is not a good idea to take Spotify’s data at face value, as the algorithms behind the API might be, to some extend, unsound. As mentioned in the Audio Feature Overview, danceability is one of the mysterious features that might raise an issue of construct validity. Nevertheless, Spotify API provides a lot of valuable features that can be used for MIR and musicological research.

I would like to mention one drawback, that is the fact that lyrics were not taken into account for this research. Lyrical content is of crucial importance to Rammstein, as Till poetically sings about taboo topics, such as cannibalism (Mein Teil), stalking (Du Riechst So Gut) and pyromania (Benzin). Most of the lyrics are inspired or based on real-life events. Conducting sentiment analysis on the lyrics and analyzing Spotify API’s provided features would be useful for future research. Such a combination would control for a possible confounding variable, that is lyrical content, that might have altered the internal validity of the current research.

I hope that this informal exploratory research can motivate at least one person to look into Spotify and investigate their favourite musician’s discography. I believe that the analysis techniques and findings provide insights for any curious reader on how Spotify Audio Features can be used to analyze audio on different levels. The majority know Spotify merely as a streaming service, but it is truly amazing at how much information is freely available and can be extracted from Spotify API!